1. Field of the Invention
The present invention relates to calibration of camera parameters for converting a world coordinate system, which indicates a position in the real space, to a coordinate system used in an image and vice versa.
2. Description of the Related Art
For calculating camera parameters, various methods have been proposed, for example, in Zhengyou Zhang, “A Flexible New Technique for Camera Calibration”, Technical Report MSR-TR-98-71(Document 1) and Zhengyou Zhang, “Camera Calibration With One-Dimensional Objects”, Technical Report MSR-TR-2001-120(Document 2) and Dirk Farin, et al., “Robust Camera Calibration for Sport Videos using Court Models”. Proc. SPIE Storage and Retrieval Methods and Applications for Multimedia 2004, Vol. 5307, pp. 80-91. (Document 3).
For calibration, the method disclosed in Document 1 utilizes a grid pattern in an object in an image, and the method disclosed in Document 2 utilizes a stick shaped object in an image. Therefore, these methods can be applied, only if there is such an object. The method disclosed in Document 3 utilizes cross points such as cross points of lines in the field of sports game, and the application is restricted. Further, there are some possibilities of false detection of a line out of the field as a line in the field.
In image processing field, a method using feature points, which are extracted based on pixel values, is well known to find out corresponding pixel pairs in two images. To extract feature points from an image, Scale Invariant Feature Transform (SHIFT) method and Affine Region Detection method are well known. The SHIFT method is disclosed in David G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints”, Computer Science Department University of British Columbia, and Affine Region Detection method is disclosed in K. Mikolajczyk, et al., “A Comparison of Affine Region Detectors”, International Journal of Computer Vision 2006. Further, cross points in document 3 can be used as feature points.
For searching corresponding pixel pairs among feature points, Approximate Nearest Neighbor method can be used in addition to SHIFT method. Approximate Nearest Neighbor method is disclosed in Sunil Arya, et al., “An optimal Algorithm for Approximate Nearest Neighbor Searching in Fixed Dimensions”, Proc. the fifth annual ACM-SIAM Symposium on Discrete Algorithms, pp. 573-582.
Corresponding pixels, which are searched by using above methods, includes errors, and it is required to remove false pixel pairs. To remove false pixel pairs, Least Median of Square (LMedS) method and Random Sample Consensus (RANSAC) method are known. RANSAC method is disclosed in Martin A. Fischler, et al., “Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography”, Communications of the ACM, Vol. 24, Number 6. However these methods cannot remove misdetected pixel pairs completely.